Automatically identifying pressure injuries

ABSTRACT

Various systems, devices, and methods for predicting and identifying root causes of pressure injuries in a clinical environment are described. An example method includes identifying, based on an electronic medical record (EMR) of a patient, that the patient has an injury; receiving, from one or more sensors, sensor data indicating one or more parameters of the patient; determining, based on the sensor data, at least one root cause of the injury; and transmitting, to an external computing device, a report indicating the at least one root cause of the injury.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority of U.S. Provisional Application No.63/084,518, which was filed on Sep. 28, 2020 and is incorporated byreference herein in its entirety.

TECHNICAL FIELD

This application relates generally to predicting and/or identifyingpressure injuries experienced by patients in a hospital environment. Insome cases, the root causes of pressure injuries can be derived.

BACKGROUND

Care facilities, such as hospitals, hospices, and the like, may managenumerous patients using a handful of care providers. For example, ahospital may have hundreds or even thousands of patients residing inin-patient wards, intensive care units (ICUs), and the like. Thesepatients are managed by various care providers, including nurses,medical technicians, physician assistants, physicians, residents,fellows, and medical students.

Patients may develop pressure injuries in a care facility due topressure and/or friction between skin and other objects. For example, animmobile patient may develop a pressure injury due to pressure betweenthe patient and a bed on which the patient is resting. In some cases, apatient in contact with a medical device (e.g., an oxygen cannula ortubing) may develop a pressure injury due to pressure and/or frictionbetween the skin of the patient and the medical device. Additionally,various risk factors increase the risk of developing a pressure injury.For example, the presence of moisture on the skin of the patient canincrease the patient's risk of developing a pressure injury.

Care facilities have a significant incentive to prevent patients fromdeveloping pressure injuries. Pressure injuries can cause substantialdiscomfort to patients, and in some instances, can expose patients tosecondary infections that can be deadly. Furthermore, medical insurersand reimbursement agencies will not pay a care facility for expensesassociated with treating a pressure injury that developed while thepatient was being cared for by the care facility. Care facilitiesgenerally have policies that prevents or ameliorates risk factorsassociated with developing pressure injuries. For example, careproviders may encourage the patient to move around or shift position ona periodic basis in order to prevent pressure injuries from developing.However, in complex care environments, pressure injuries may still occurwhile patients are residing in care facilities.

When a pressure injury is identified on a patient, the patient may bereferred to a wound care specialist. Wound ostomy catheter nurses(WOCNs), for example, are specialists that can diagnose the severity ofa pressure injury and treat the pressure injury. However, if other careproviders are slow to diagnose the pressure injury, even specialists mayfind the pressure injury difficult to treat. In severe cases, delayswith diagnosing and/or treating a pressure injury can cause permanentdeficits in the patient.

SUMMARY

Various implementations of the present disclosure relate to systems,devices, and methods for identifying or predicting pressure injuries. Invarious examples, at least one sensor can detect one or more parametersof a patient. The patient may be resting on a support structure, such asa hospital bed, that may cause the patient to develop a pressure injury.Furthermore, an electronic medical record (EMR) of the patient may trackone or more parameters of the patient, such as nutrition levels of thepatient. In some cases, data from the sensor(s) and/or the EMR can beanalyzed in order to predict whether the patient will develop a pressureinjury. For example, a computing model may be configured to generate ascore indicative of the risk that the patient will develop or has anundiagnosed pressure injury. In various instances, an alert indicatingthe risk and/or the score can be output to a computing device associatedwith a care provider. Accordingly, the care provider may be notifiedabout the patient's risk of developing the pressure injury before thepressure injury occurs, thereby enabling the care provider to take stepsto prevent the pressure injury from developing.

Some examples described herein relate to systems, devices, and methodsfor identifying root causes of pressure injuries that have developed.For example, once a patient is diagnosed with a pressure injury, acomputing model may be configured to parse through sensor data and/orEMR data to determine one or more significant risk factors associatedwith the patient prior to the development of the pressure injury. Atleast one root cause may be determined based on the risk factorsexperienced by the patient prior to the diagnosis of the pressureinjury. In some cases, a system outputs a report summarizing the rootcause(s) to a computing device associated with a care provider and/or anadministrator of the care facility. Thus, the care provider and/oradministrator can implement system-wide changes that can preventpatients from developing pressure injuries in the future.

Various implementations described herein can improve the technical fieldof patient management in a complex healthcare setting. By identifying apatient's risk of developing a pressure injury prior to the developmentof the pressure injury, the pressure injury can be prevented, therebyimproving patient care and reducing infection risk. Furthermore, byidentifying one or more root causes of a pressure injury of a patient,care providers can improve treatment of the pressure injury byaddressing the root cause(s). Furthermore, care providers can improvepatient management in a healthcare facility accommodating the patient bypreventing the root cause(s) of the patient's pressure injury fromcausing other pressure injuries in other patients. In various cases,pressure injuries can be predicted, identified, and analyzedautomatically based on data generated by sensors integrated with asupport structure (e.g., a bed) of the patient. Furthermore, the sensordata can be integrated with the EMR data of the patient as the patientis residing on the support structure, thereby providing the opportunityfor real-time analysis of a risk of the patient in developing pressureinjuries, without constant supervision by a care provider.

DESCRIPTION OF THE FIGURES

The following figures, which form a part of this disclosure, areillustrative of described technology and are not meant to limit thescope of the claims in any manner.

FIG. 1 illustrates an example environment for identifying pressureinjuries in at least one care facility.

FIG. 2 illustrates example signaling for identifying, predicting, andanalyzing pressure injuries in a clinical environment.

FIG. 3 illustrates an example of a datastore storing EMRs of patients ina clinical environment.

FIG. 4 illustrates an example process for training a computing model topredict a pressure injury based on sensor data and/or EMR data.

FIG. 5 illustrates an example process for predicting a pressure injuryof a patient.

FIG. 6 illustrates another example process for predicting a pressureinjury of a patient.

FIG. 7 illustrates at least one example device configured to enableand/or perform the some or all of the functionality discussed herein.

DETAILED DESCRIPTION

Various implementations of the present disclosure will be described indetail with reference to the drawings, wherein like reference numeralspresent like parts and assemblies throughout the several views.Additionally, any samples set forth in this specification are notintended to be limiting and merely set forth some of the many possibleimplementations.

FIG. 1 illustrates an example environment 100 for identifying pressureinjuries in at least one care facility. As shown, a patient 102 can beassociated with a support device 104. As used herein, the terms“patient,” “subject,” and their equivalents, can refer to a human orother animal being supported by a support device. In various cases, thepatient may be admitted to a care facility, such as a hospital, hospice,clinic, urgent care facility, physician's office, or other type ofclinical environment.

The support device 104 includes, for instance, a gurney, hospital bed,or some other structure configured to support the patient 102. As usedherein, the terms “bed,” “hospital bed,” and their equivalents, canrefer to a padded surface configured to support a patient for anextended period of time (e.g., hours, days, weeks, or some other timeperiod). The patient 102 may be laying down on the support device 104.For example, the patient 102 may be resting on the support device 104for at least one hour, at least one day, at least one week, or someother time period. In various examples, the patient 102 and the supportdevice 104 may be located in a managed care environment, such as ahospital, or a hospice. For example, the patient 102 can be located in amedical ward of a hospital, an intensive care unit (ICU) of thehospital, or some other part of a hospital. In some implementations, thesupport device 104 includes a mechanical component that can change theangle at which the patient 102 is disposed. In some cases, the supportdevice 104 includes padding to distribute the weight of the patient 102on the support device 104. According to various implementations, thesupport device 104 can include vital sign monitors configured to outputalarms or otherwise communicate vital signs of the patient 102 toexternal observers (e.g., care providers, family members, and the like).The support device 104 may include railings that prevent the patient 102from sliding off of a resting surface of the support device 104. Therailings may be adjustable, in some cases.

The support device 104 further includes various sensors. For example,the support device 104 includes one or more load cells 106. The loadcell(s) 106 may be configured to detect a pressure on the support device104. In various cases, the load cell(s) 106 can include one or morestrain gauges, one or more piezoelectric load cells, a capacitive loadcell, an optical load cell, any device configured to output a signalindicative of an amount of pressure applied to the device, or acombination thereof. For example, the load cell(s) 106 may detect apressure (e.g., weight) of the patient 102 on the support device 104. Insome cases, the support device 104 includes multiple load cells thatrespectively detect different pressures on the support device 104 indifferent positions along the support device 104. In some instances, thesupport device 104 includes four load cells arranged at four corners ofa resting surface of the support device 104, which respectively measurethe pressure of the patient 102 on the support device 104 at the fourcorners of the support device 104. The resting surface, for instance,can be a surface in which the patient 102 contacts the support device104, such as a top surface of the support device 104.

The support device 104 may include one or moisture sensors 108. Themoisture sensor(s) 108 may be configured to measure a moisture on asurface (e.g., the resting surface) of the support device 104. Forexample, the moisture sensor(s) 108 can include one or more capacitancesensors, one or more resistance sensors, one or more thermal conductionsensors, or a combination thereof. In some cases, the moisture sensor(s)108 include one or more fiber sheets configured to propagate moisture tothe moisture sensor(s) 108. In some cases, the moisture sensor(s) 108can detect the presence or absence of moisture (e.g., sweat or otherbodily fluids) disposed between the support device 104 and the patient102.

In various examples, the support device 104 can include one or moretemperature sensors 110. The temperature sensor(s) 110 may be configuredto detect a temperature of the patient 102 and/or the support structure104. In some cases, the temperature sensor(s) 110 includes one or morethermistors, one or more thermocouples, one or more resistancethermometers, one or more Peltier sensors, or a combination thereof.

The support device 104 may include one or more cameras 112. Thecamera(s) 112 may be configured to capture images of the patient 102,the support structure 104, or a combination thereof. In various cases,the camera(s) 112 may include radar sensors, infrared cameras, visiblelight cameras, depth-sensing cameras, or any combination thereof. Insome examples, infrared images may indicate, for instance, a temperatureprofile of the patient 102 and/or the support structure 104. Thus, thecamera(s) 112 may be a type of temperature sensor. In addition, theimages may indicate a position of the patient 102 and/or the supportstructure 104, even in low-visible-light conditions. For example, theinfrared images may capture a position of the patient 102 during a nightenvironment without ambient lighting in the vicinity of the patient 102and/or the support structure 104. In some cases, the camera(s) 112 mayinclude one or more infrared video cameras. The camera(s) 112 mayinclude at least one depth-sensing camera configured to generate avolumetric image of the patient 102, the support structure 104, and theambient environment. According to various implementations, the imagesand/or videos captured by the camera(s) 112 are indicative of a positionand/or a movement of the patient 102 over time.

According to some examples, the support device 104 can include one ormore video cameras 114. The video camera(s) 114 may be configured tocapture videos of the patient 102, the support structure 104, anentrance to a room containing the support structure 104, an entrance toa bathroom adjacent to the room containing the support structure 104, ora combination thereof. The videos may include multiple images of thepatient 102 and/or the support structure 104. Thus, the videos capturedby the video camera(s) 114 may be indicative of a position and/ormovement of the patient 102 over time. In some examples, the videocamera(s) 114 capture visible light videos, changes in radar signalsover time, infrared videos, or any combination thereof.

In some examples, the support structure 104 includes a head rail and afoot rail. The camera(s) 112 and/or video camera(s) 114, for instance,are mounted on the head rail, the foot rail, an extension (e.g., a metalstructure) attached to the head rail or the foot rail, or anycombination thereof. In various implementations, the camera(s) 112and/or video camera(s) 114 are attached to a wall of the room containingthe support structure 104. In some examples, the camera(s) 112 and/orvideo camera(s) 114 are attached to a cart (e.g., a support structure onwheels) that is located in the vicinity of the support structure 104.

In various cases, the sensors (e.g., the load cell(s) 106, the moisturesensor(s) 108, the temperature sensor(s) 110, the camera(s) 112, thevideo camera(s) 114, or any combination thereof) of the support device104 are configured to monitor one or more parameters of the patient 102and to generate sensor data associated with the patient 102. In variouscases, the sensors convert analog signals (e.g., pressure, moisture,temperature, light, electric signals, or any combination thereof) intodigital data that is indicative of one or more parameters of the patient102. As used herein, the terms “parameter,” “patient parameter,” andtheir equivalents, can refer to a condition of an individual and/or thesurrounding environment. In this disclosure, a parameter of the patient102 can refer to a position of the patient 102, a movement of thepatient 102 over time (e.g., mobilization of the patient 102 on and offof the support device 104), a pressure between the patient 102 and anexternal object (e.g., the support device 104), a moisture level betweenthe patient 102 and the support device 104, a temperature of the patient102, a vital sign of the patient 102 (e.g., a blood pressure, aperipheral oxygenation level, a heart rate, a respiration, a bloodvolume, or some other vital sign), a nutrition level of the patient 102,a medication administered and/or prescribed to the patient 102 (e.g., avasopressor or any other medication that impacts pressure injury risk),a previous condition of the patient 102 (e.g., the patient was monitoredin an ICU, in dialysis, presented in an emergency department waitingroom, etc.), a skin tone of the patient 102 (e.g., which may impactwhether care providers readily identify pressure injuries on the skin ofthe patient 102), circulation of the patient 102 (e.g., restricted bloodflow), a pain level of the patient 102, the presence of implantable orsemi-implantable devices (e.g., ports, tubes, catheters, other devices,etc.) in contact with the patient 102, or any combination thereof.Optionally, a parameter is specific to a limb, such as an arm or a legof the patient 102. In various examples, the support device 104generates data indicative of one or more parameters of the patient 102using the load cell(s) 106, the moisture sensor(s) 108, the temperaturesensor(s) 110, the cameras 112, the video camera(s) 114, or acombination thereof. The data generated by the support device 104 bereferred to herein as “sensor data.”

The support device 104 may further include a transmitter 116. Thetransmitter 116 may be configured to transmit the sensor data from thesupport device 104 over one or more communication networks 118. Thecommunication network(s) 118 may include one or more wired networks(e.g., at least one wired local area network (LAN), at least onefiber-optic network, or some other type of wired network), one or morewireless networks (e.g., at least one wireless LAN, at least one WI-FInetwork, at least one BLUETOOTH network, at least one cellular network,or some other type of wireless network), or a combination thereof. Insome cases, the communication network(s) 118 include one or more widearea networks (WANs), such as the Internet. In some cases, thetransmitter 116 includes a transceiver configured to receive signalsfrom the communication network(s) 118.

In some examples, the sensors (e.g., the load cell(s) 106, the moisturesensor(s) 108, the temperature sensor(s) 110, the camera(s) 112, thevideo camera(s) 114, or any combination thereof) can includecontact-free continuous monitoring (CFCM) sensors. For instance, asensing pad including the sensors integrated or otherwise attached to asupport substrate (e.g., a flexible substrate) can be placed above orbelow a mattress of the support device 104. Accordingly, the patient 102may move freely without being encumbered by the presence of the sensors.In some cases, the sensing pad is removable from the support device 104.The sensing pad, in some cases, includes the transmitter 116. Forinstance, the sensing pad may include transmitter(s) that transmit dataover the communication network(s) 118.

According to various implementations described herein, the supportdevice 104 may transmit the sensor data to an identification system 120over the communication network(s) 118. The identification system 120 maybe configured to transmit and/or receive data over the communicationnetwork(s) 118. The identification system 120 may be embodied inhardware, software, or a combination thereof. In some cases, theidentification system 120 is hosted on one or more computing deviceslocated in a local environment of the support device 104. For example,the identification system 120 may be hosted on an on-premises serverlocated in a hospital in which the support device 104 is also located.In some cases, the identification system 120 is hosted by one or morecomputing devices located remotely from the premises of the supportdevice 104. For example, the identification system 120 may be hosted ina distributed computing environment, such as a cloud computingenvironment.

The environment 100 may also include an electronic medical record (EMR)system 122. The EMR system 122 may be configured to transmit and/orreceive data over the communication network(s) 118. The EMR system 122may be embodied in hardware, software, or a combination thereof. In someexamples, the EMR system 122 is hosted on one or more computing deviceslocated in a local environment of the support device 104. For example,the EMR system 122 may be located on an on-premises server located in ahospital in which the support device 104 is also located. In some cases,the EMR system 122 is hosted by one or more computing devices locatedremotely from the premises of the support device 104. For example, theEMR system 122 may be hosted in a distributed computing environment,such as a cloud computing environment. In some cases, the EMR system 122and the identification system 120 are hosted by the same computingdevice(s).

The EMR system 122 may be configured to store and/or manage medicalrecords associated with a population of patients that include thepatient 102. The EMR system 122 may include a datastore 124 that storesthe medical records of the patients. The datastore 124, for instance,can include one or more databases. As used herein, the terms “electronicmedical record,” “EMR,” “electronic health record,” “EHR,” and theirequivalents, can refer to a collection of digital data indicative of themedical status and/or medical history of an individual patient. An EMRof the patient 102, for instance, can indicate the immunization recordof the patient 102, diagnostic images taken of the patient 102, a listof medications of the patient 102, a list of allergies of the patient102, demographics of the patient 102, laboratory tests of the patient102, previous visits of the patient 102 to a healthcare setting (e.g., ahospital, a clinic, or another type of healthcare setting), proceduresperformed on the patient 102, a medical history of the patient 102(e.g., whether the patient 102 was previously on dialysis, using afeeding tube, in the emergency department, etc.), and so on. The EMRsystem 122 can therefore store any of the types of parameters that arediscussed herein.

In various cases, the EMRs managed by the EMR system 122 can begenerated and/or modified by medical personnel, such as a care provider126. The care provider 126, for instance, may be associated with aclinical device 128. The clinical device 128 may include a computingdevice, such as a desktop computer, a laptop computer, a tabletcomputer, a smartphone, or any other type of user equipment (UE). Invarious examples, the care provider 126 can generate and/or modify(e.g., update) the EMRs of various patients (including the patient 102)that are stored by the EMR system 122. For example, the care provider126 may measure a vital sign (e.g., a blood pressure) of the patient102. The care provider 126, via a web browser of the clinical device 128and/or an application of the clinical device 128, may enter the vitalsign. The clinical device 128 may transmit, to the EMR system 122 overthe communication network(s) 118, data indicating the vital sign. Inresponse to receiving the data, the EMR system 122 may update the EMR ofthe patient 102 to indicate the measured vital sign.

According to some examples, the care provider 126 can enter notes intothe EMRs. As used herein, the term “note,” and is equivalents, can referto text indicative of observations and/or care provided to a patient bya care provider. For example, the care provider 126 may observe thepatient 102 and may enter, using the clinical device 128, a noteexplaining a condition of the patient 102, tests performed by thepatient 102, ongoing monitoring performed on the patient 102, proceduresperformed on the patient 102, diagnoses of the patient 102, symptoms ofthe patient 102, and the like. Notes associated with the patient 102 maybe stored, by the datastore 124, in the EMR of the patient 102.

In some cases, the identification system 120 and/or the EMR system 122may be configured to perform natural language processing on notes storedin the datastore 124. For example, the identification system 120 and/orthe EMR system 122 may be configured to analyze a note in the EMR of thepatient 102 to recognize words and/or phrases associated with a sensoryresponse of the patient 120 (e.g., “sensitivity,” “responsiveness,”“sensitive to touch,” etc.). The EMR data may encompass recognized wordsin notes of the EMRs stored in the datastore 124.

In various examples, the EMRs managed by the EMR system 122 can beviewed and/or modified by various devices, such as the clinical device128. In some cases, the EMRs can be accessed by devices in differentlocations, hospital systems, insurance companies, care facilities, orother types of environments.

In some implementations, the identification system 120 is configured togenerate, manage, and/or adjust a computing model 130 based on thesensor data and/or the EMRs stored by the EMR system 122. In variouscases, the computing model 130 is configured to identify a risk that thepatient 102 will or has developed a pressure injury based on the sensordata from the support device 104 and/or the EMR of the patient 102stored in the datastore 124 of the EMR system 122.

As used herein, the term “pressure injury,” and its equivalents, canrefer to any injury caused due to pressure and/or friction between theskin of a patient and the surface of an object. Pressure sores andpressure ulcers are examples of pressure injuries. Pressure injuries canbe defined by stages. A Stage 1 pressure injury can refer to an area ofskin that is warmer than surrounding tissue, firmer or softer thansurrounding tissue, reddened, resistant to blanching, painful, but maynot be an open wound. A Stage 2 pressure injury can refer to an openingor fluid-filled blister in skin (e.g., an ulcer) that is tender andpainful. A Stage 3 pressure injury can refer to a sore that extends intotissue below the skin, such that fat may be visible within the sore. AStage 4 pressure injury can refer to a wound that reaches from the skininto muscle and/or bone and can cause extensive damage to the patient.

Various factors affect a risk of developing a pressure injury. TheBraden Scale, for example, utilizes factors such as sensory perception,moisture, activity, mobility, nutrition, friction, and shear to estimatea patient's risk of developing a pressure ulcer. Temperature may alsoimpact a risk of developing a pressure injury and/or may be indicativeof a pressure injury that has already developed. The sensor data fromthe support device 104 and/or the EMR from the EMR system 122 cancorrespond to factors that impact the risk that the patient 102 has orwill develop a pressure injury. For example, the sensor data generatedby the moisture sensor(s) 108 may correspond to an exposure of thepatient 102 to moisture; the sensor data generated by the load cell(s)106, camera(s) 112, and video camera(s) 116 can correspond to anactivity, mobility, friction, and/or shear of the patient 102 withrespect to the support device 104 and/or another type of object (e.g.,an implantable device, a semi-implantable device, etc.); the EMR of thepatient 102 may include data corresponding to the nutrition of thepatient 102; and the sensor data generated by the temperature sensor(s)110 and/or camera(s) 112 can correspond to the temperature of thepatient 102. In some cases, the sensory perception of the patient 102can be identified based on notes stored in the EMR of the patient 102.For example, the care provider 126 may assess the sensory perception ofthe patient 102 and record one or more assessments of the sensoryperception of the patient 102 in the EMR of the patient 102.

According to some implementations, the computing system 120, whenexecuting the computing model 130, calculates a score indicating a riskof the patient 102 for developing and/or having a pressure injury. Asused herein, the terms “aggregate score,” “score,” and theirequivalents, can refer to at least one numerical indicator. For example,the computing model 130 may include the following Formula 1 to generatethe score:

S=f _(m)(s _(m))+f _(a)(s _(i) ,s _(ir) ,s _(c))+f _(n)(r _(n))+f _(t)(s_(ir) ,s _(t))+f _(s)(r _(s))   Equation 1

wherein S is the score indicative of the risk of the patient 102 forhaving and/or developing a pressure injury, f_(m)( ) is a function forcalculating the factor of moisture in the risk, s_(m) is sensor datafrom the moisture sensor(s) 108, f_(a)( ) is a function for calculatingthe factor of activity, mobility, friction, and/or shear in the risk,s_(l) is the sensor data from the load cell(s) 106, s_(ir) is the sensordata from the camera(s) 112, s_(c) is the sensor data from the videocamera(s) 116, f_(n)( ) is a function for calculating the factor ofnutrition in the risk, r_(n) is a nutrition record of the patient 102stored in the EMR of the patient 102, f_(i)( ) is a function forcalculating the factor of temperature in the risk, s_(t) is the sensordata from the temperature sensor(s) 110, f_(s)( ) is a function forcalculating the factor of sensory perception in the risk, and r_(s) is asensory perception record of the patient 102 stored n the EMR of thepatient 102. In some cases, one or more of f_(m)( ), f_(a)( ), f_(n)( ),f_(t)( ), or f_(s)( ) can be omitted from Equation 1. In some examples,the computing model 130 may generate a Braden Score of the patient 102based on the sensor data from the support device 104 and/or the EMR ofthe patient 102 stored in the datastore 124 of the EMR system 122.

According to some examples, the computing model 130 can be a machinelearning model and can be trained to identify the risk based on trainingdata. As used herein, the term “machine learning model,” and itsequivalents, can refer to a computer-based model configured to identifypatterns in data, and which can improve its pattern recognition based ontraining data. For example, the computing model 130 can include at leastone of a deep learning model, a linear regression model, a logisticregression model, a gradient boost machine, or some other machinelearning model. In various cases, the training data can include sensordata from multiple support devices (including or excluding the supportdevice 104) and EMR data from multiple EMRs, which may be associatedwith multiple patients (including or excluding the patient 102). Thetraining data may also indicate whether those patients developedpressure injuries, the severity of the pressure injuries, and the like.Using the training data, one or more numerical factors of the computingmodel 130 can be optimized to accurately predict the risk of pressureinjuries based on sensor data and EMR data. This process of optimizationcan be referred to as “training.” Once trained, the computing model 130can be configured to accurately output the risk of the patient 102 fordeveloping and/or having a pressure injury based on inputs including oneor more parameters of the patient 102, which may be indicated by thesensor data from the support device 104 and/or the EMR of the patient102 from the EMR system 122.

In some cases, the identification system 120 is configured to determinewhether the patient 102 is at risk for a pressure injury based on thesensor data. For example, the sensor data from the support device 104and or the EMR of the patient 102 may be input into the (e.g.,pre-trained) computing model 130, such that the identification system120 can determine the risk of the patient 102 for developing and/orhaving the pressure injury using the computing model 130. In some cases,the identification system 120 may store the risk locally and/or in theEMR of the patient 102, such that the care provider 126 can access therisk on-demand using the clinical device 128.

In some implementations, the identification system 120 can selectivelygenerate an alert based on the risk. The identification system 120 maycompare the risk to a threshold. Based on the comparison, theidentification system 120 may generate and/or output an alert to the EMRsystem 122 and/or the clinical device 128. The alert may indicate thatthe patient 102 is at a relatively high risk for developing and/orhaving a pressure injury. In some cases, the identification system 120may identify, based on the sensor data, a location of the potentialand/or suspected pressure injury on the skin of the patient 102. In someexamples, the alert may further indicate one or more potential causes ofthe pressure injury. For example, the identification system 120 maycompare f_(m)( ), f_(a)( ), f_(n)( ), f_(t)( ), or f_(s)( ) to athreshold, and report moisture, activity, nutrition, temperature, orsensory perception as a potential cause of the pressure injury based onthe comparison. In some cases, the alert may be output by the clinicaldevice 128 to the care provider 126, causing the care provider 126 toprovide assistance to the patient 102, thereby preventing the pressureinjury or preventing the pressure injury from getting worse.

In particular examples, the score representing the risk is proportionalto the risk of the patient 102 to having and/or developing a pressureinjury. For instance, the score may be in a range from 0 to 100, whereina score of 0 represents a certainty that the patient 102 is notdeveloping a pressure injury and a score of 100 represents a certaintythat the patient 102 has a pressure injury. The threshold may be 50. Ifthe patient 102 has a score that is greater than 50, then theidentification system 120 may generate an alert indicating that thepatient 102 is in danger of developing a pressure injury and maytransmit the alert to the clinical device 128. If the contribution ofmoisture to the score is greater than a threshold (i.e., if f_(m)( )exceeds another threshold), then the alert may be generated to indicatethat moisture is a significant factor in risk of the patient 102 fordeveloping a pressure injury. If the patient 102 has a score that isless than or equal to 50, then the identification system 120 may refrainfrom generating and/or transmitting the alert. In some cases, the scoremay exceed the threshold before the patient 102 actually develops thepressure injury. Accordingly, in some examples described herein, thepressure injury of the patient 102 can be prevented by the care provider126.

According to some implementations, the alert indicates one or morerecommended interventions to treat and/or prevent a pressure injury ofthe patient 102. The recommended interventions include, for instance, aninstruction for safe handling of the patient 102, providing an improvedsurface (e.g., placing a cushion, tape, or the like between an object,such as the support device 104, an implantable device, or asemi-implantable device, and the patient 104), consulting an expert(e.g., a WOCN), scheduling a consultation (e.g., in-person or virtualvia the computing device 128) with the expert, turning the patient 102,adding or replacing pads (e.g., incontinence pads disposed on thesupport structure 104), mobilizing the patient 102, adjusting nutritionof the patient 102, modifying an incontinence pad changing schedule, ora combination thereof. In some cases, the alert includes recommendedinterventions that are based on one or more parameters of patient 102.For example, if the risk of the patient 102 for developing a pressureinjury based on moisture is above a threshold, then the identificationsystem 120 generates the alert to specifically include recommendedinterventions specific to addressing moisture (e.g., adding or replacingpads, modifying an incontinence pad changing schedule, etc.).

In some examples, the score is generated with respect to a particularbody part of the patient 102 (e.g., a limb of the patient 102), and thealert indicates that the particular body part of the patient 102 has oris in danger of developing a pressure injury. For instance, the scorecan be generated based on a parameter detected by an infrared or heatsensor monitoring an arm of the patient 102, and the alert mayspecifically indicate that the arm of the patient 102 is in danger ofdeveloping a pressure injury. In some cases, the score can be generatedbased on a procedure (e.g., intubation, ostomy, etc.) performed on thebody part of the patient 102 and the alert can identify the body partimpacted by the procedure. In some cases, the recommended interventionsincluded in the alert are specific to addressing the risk of pressureinjury of the body part.

In some implementations, the score is generated with respect to a groupof patients including the patient 102. For example, the identificationsystem 120 generates scores indicating the risk that each patient in thegroup has and/or will develop a pressure injury and aggregates thescores. The aggregated score may indicate the risk that the group, as awhole, have and/or will develop pressure injuries. In some cases, theidentification 120 can generate the score based on a history of woundswithin the group, a type of unit (e.g., ICU, in-patient ward, a floor ofa hospital, etc.) that the group is associated with, staffing of theunit, whether the unit is accepting overflow from other units, seasonaltrends associated with the unit, a number of pressure injury cases thathave been diagnosed within the unit in a particular time period (e.g.,the previous day, week, etc.), or a combination thereof. Theidentification system 120 can identify recommended interventionsincluding adding additional staff to the unit, adding specialized staff(e.g., WOCNs) to the unit, ordering and/or delivering specializedequipment to the unit that can prevent wounds (e.g., lifts, walkers,specialized versions of the support device 104, etc.), or a combinationthereof.

In various implementations described herein, the identification system120 can identify that the patient 102 has a pressure injury and use thesensor data from the support device 104 and/or the EMR of the patient102 to identify one or more root causes of the pressure injury. As usedherein, the term “root cause,” and its equivalents, can refer to a riskfactor that is likely to have caused a known pressure injury.

According to some examples, the identification system 120 may identifythat the patient 102 has a pressure injury based on the EMR of thepatient 102 and/or a message from the clinical device 128. For example,the care provider 126 may observe a pressure injury on the patient 102and may generate a note indicating the pressure injury in the EMR of thepatient 102 that is stored in the datastore 124 of the EMR system 122.The identification system 120 and/or the EMR system 122 may performnatural language processing on the note in order to enable theidentification system 120 to identify the pressure injury.

Once the pressure injury of the patient 102 is identified, theidentification system 120 can identify one or more root causes of thepressure injury. The identification system 120 may identify sensor datafrom the support device 104 obtained prior to the identification of thepressure injury (e.g., by the care provider 126 and/or theidentification system 120). In addition, the identification system 120may access EMR data corresponding to the condition of the patient 102and/or care performed on the patient 102 prior to the identification ofthe pressure injury. The identification system 120 may perform apost-hoc analysis on the sensor data and/or the EMR data to identify theone or more root causes of the pressure injury. In some cases, theidentification system 120 can calculate a contribution of a potentialroot cause to the pressure injury risk of the patient 102. Potentialroot causes can include, for example, the presence of moisture incontact with the patient 102, an amount of moisture in contact with thepatient 102, an amount of time that moisture was in contact with thepatient 102, an amount of pressure between the patient 102 and thesupport device 104, an amount of time that greater than a thresholdpressure was between the patient 102 and the support device 104 withoutinterruptions, an amount of time that the patient 102 rested on thesupport device 104 without moving, an amount of time that the patient102 rested on the support device without getting up from the supportdevice 104, an amount of pressure between the patient 102 and thesupport device 104 during movement of the patient 102, an amount ofrubbing between the patient 102 and the support device 104, atemperature of the patient 102 (e.g., at a point of contact between thepatient 102 and the support device 104), a nutrition level of thepatient 102, a sensitivity of the patient 102 to physical examination, apresence and/or duration of implantable or semi-implantable devices incontact with the patient 102, a duration of a surgical procedureperformed on the patient 102, an amount of time that the patient 102 wasin a particular position (e.g., an incline position) during a procedure,and the like. Semi-implantable devices include, for instance, cathetersplaced in the patient 102, drains placed in the patient 102, and tubes(e.g., ventilation tubes) placed in the patient 102. Implantable andsemi-implantable devices are example sources of pressure injuries. Insome cases, potential root causes can be identified by the computingmodel 130 using machine learning. For example, the computing model 130may be trained to optimize correlations between the potential rootcauses and risks of pressure injuries based on training data thatincludes sensor data and/or EMR data from other patients who haveexperienced pressure injuries.

According to some examples, the computing model 130 includes a componentmodel for each potential root cause. By inputting at least a componentof the sensor data and/or the EMR data into a component model, thecomponent model may output a statistic that corresponds to the estimatedcontribution of the associated potential root cause on the pressureinjury. For example, a component model associated the potential rootcause of “an amount of pressure between the patient 102 and the supportdevice 104 during movement of the patient 102” may be in put with dataobtained by the load cell(s) 106 (indicating the amount of pressure),data obtained by the camera(s) 112 (indicating the movement), and dataobtained by the video camera(s) 116 (indicating the movement). In somecases, the statistic for each root cause may be between 0 and 100,wherein 0 represents a certainty that the potential root cause did notcontribute to the pressure injury and 100 represents a certainty thatthe potential root cause did cause the pressure injury. According tosome examples, each potential root cause whose output from itsassociated component model exceeds a threshold (e.g., 50) may bedetermined to be an actual root cause of the pressure injury. In somecases, one or more of the potential root causes whose component modelsoutput the highest statistics may be determined to be root cause(s) ofthe pressure injury. For example, the three highest statistics of thetotal group of root causes may be determined to be the three root causesof the pressure injury.

In various cases, the identification system 120 may generate a reportindicating the root cause(s) of the pressure injury of the patient 102.The report may be transmitted to the clinical device 128 or to someother computing device, such as a computing device associated with afamily member of the patient 102. The clinical device 128 may output thereport to the care provider 126, thereby providing the care provider 126with context about the pressure injury. According to some examples, theother computing device may output the report to user (e.g., the familymember), and prompt the user for confirmation of the pressure injury. Insome examples, the clinical device 128 and/or other computing device mayprompt a user to input a picture of the skin of the patient 102 at thelocation of the suspected pressure injury. In some cases, theidentification system 120 may transmit the report to the EMR system 122,which may store the report in the EMR of the patient 102.

Although not illustrated in FIG. 1, in some cases, the identificationsystem 120 can utilize data from multiple support devices in order toidentify risks of various patients resting on the multiple supportdevices. In some examples, the identification system 120 can outputalerts and/or reports to multiple clinical devices associated withmultiple care providers. In some cases, the identification system 120can generate a single report indicating one or more common root causesof pressure injuries of a group of patients whose care was managed by asingle care facility (e.g., a hospital). The report could be output to adevice associated with an administrator of the facility. Thus, theadministrator can implement changes to policies within the care facilityto address common sources of pressure injuries, thereby preventingfuture pressure injuries in patients managed by the care facility.

FIG. 2 illustrates example signaling 200 for identifying, predicting,and analyzing pressure injuries in a clinical environment. As shown, thesignaling 200 involves the identification system 120, the EMR system122, the datastore 124, and the computing model 130 described above withreference to FIG. 1. Further, the signaling involves first to nthclinical devices 128-1 to 128-n, which may include the clinical device128 described above with reference to FIG. 1. The signaling 200 may alsoinvolve first to mth support devices 104-1 to 104-m, which may includethe support device 104 described above with reference to FIG. 1. Thevalues of n and m may each be positive integers.

In various examples, the first to nth clinical devices 128-1 to 128-nmay be operated by various users within the clinical environment. Forexample, the clinical devices 128-1 to 128-n may be carried, operated,viewed, or otherwise utilized by care providers, such as physicians,physician assistants, nurses, medical technicians, and the like. In someexamples, the first to nth clinical devices 128-1 to 128-n include oneor more UEs, mobile phones, tablet computers, laptop computers, desktopcomputers, servers, or other types of computing devices. In some cases,the first to nth clinical devices 128-1 to 128-n include at least onedisplay that can output alerts to a team of care providers. Forinstance, the first to nth clinical devices 128-1 to 128-n may includeone screen simultaneously displaying the statuses of multiple patientsat a nurse station, doctor's lounge, or other shared space in theclinical environment. In some cases, the first to nth clinical devices128-1 to 128-n may include one or more medical devices, such as magneticresonance imaging (MRI) scanners, surgical robots, vital sign monitors,or other types of medical devices.

In various examples, the first to nth clinical devices 128-1 to 128-ntransmit clinical data 202 to the EMR system 122. The clinical data 202can include any data indicative of a status or a condition of a patientin the clinical environment. For example, the clinical data 202 caninclude patient information (e.g., demographics, identity, age, etc.),medical histories (e.g., previous surgical procedures, treatments,conditions, etc.), test results (e.g., electrolyte levels, vital signmeasurements, etc.), imaging (e.g., x-ray images, ultrasound images,etc.), care plans, notes, and the like. In some cases, the first to nthclinical devices 128-1 to 128-n execute an applications and/or browsersthat provide user interfaces (UIs) for various care providers. Theclinical data 202 can be generated by the first to nth clinical devices128-1 to 128-n based on user inputs to the first to nth clinical devices128-1 to 128-n. The user inputs can be input through various user inputdevices, such as keyboards, touchscreens, at the like.

The EMR system 122 may sort the clinical data 202 based on patient andstore the sorted clinical data 202 in respective EMRs of the patients.The EMRs may be stored in the datastore 124. In some cases, the clinicaldata 202 includes multiple data flows and/or packets that are taggedaccording to patient. For example, a patient may be associated with aunique identifier (e.g., a string, a number, a code, or another type ofindicator) that is included in each data packet carrying medicalinformation of the patient. Thus, the EMR system 122 may store variousportions of the clinical data 202 by patient in appropriate EMRs.

According to some implementations, the EMR system 122 may extractportions of the clinical data 202 and return the clinical data 202 tothe first to nth clinical devices 128-1 to 128-n. For example, any ofthe first to nth clinical devices 128-1 to 128-n may receive an inputrequesting at least a portion of an EMR of a particular patient, and acorresponding request can be transmitted to the EMR system 122. The EMRsystem 122 may transmit the clinical data 202 to the requesting one ofthe first to nth clinical devices 128-1 to 128-n, thereby enabling careproviders to view EMRs of their patients.

In some cases, the EMR system 122 may transmit EMR data 204 to theidentification system 120. The EMR data 204 may include a least aportion of the data stored in the EMRs of the datastore 124. Forexample, the EMR data 204 includes at least some of the clinical data202. In some cases, the EMR data 204 includes data related to risks ofthe patients associated with having and/or developing pressure injuries.In some cases, the EMR data 204 includes notes written by the careproviders. According to some implementations, the EMR system 122 mayperform natural language processing on the notes in the clinical data202, identify words indicative of pressure injuries and/or causes ofpressure injuries in the notes, and may include indications of thosewords in the EMR data 204. In some examples, the EMIR data 204 indicatespatients diagnosed with pressure injuries, physical therapies performedon the patients that may prevent pressure injuries, nutrition levels ofthe patients, sensory perception reports of the patients, and the like.Nutrition levels, for instance, can include levels (e.g.,concentrations) of molecules measured in blood, serum, or other bodilyfluids (e.g., serum albumin, prealbumin, transferrin, retinal-bindingprotein, etc.); levels of molecules in food and/or fluids administeredto the patients (e.g., calories, protein levels, zinc, Vitamin A,Vitamin C, Vitamin D, arginine, glutamine, etc.); and/or othercharacteristics of the patients related to nutrition (e.g., hydrationlevels of the patient, weight changes of the patient, etc.).

In addition, the first to mth support devices 104-1 to 104-m maytransmit sensor data 206 to the identification system 120. The first tomth support devices 104-1 to 104-m may be hospital beds that includesensors. For example, the first to mth support devices 104-1 to 104-mmay include one or more load cells, one or more moisture sensors, one ormore temperature sensors, one or more infrared cameras, one or morevideo cameras, or any combination thereof. The sensor data 206 may begenerated based on measurements by the sensors of the first to mthsupport devices 104-1 to 104-m. The various parameters detected by thesensors may be indicative of pressure injury risks of the patientssupported by the first to mth support devices 104-1 to 104-m. In variouscases, the EMR data 204 and the sensor data 206 may correspond to thesame patients.

In various cases, the identification system 120 may train the computingmodel 130 based on the EMR data 204 and/or the sensor data 206. Forexample, the identification system 120 may optimize one or more factorsof the computing model 130 to enable the computing model 130 toaccurately predict a risk of a patient in developing a pressure injurybased on the EMR data 204 and/or the sensor data 206 of the patient.Accordingly, the identification system 120 may be optimized to correlatethe EMR data 204 and the sensor data 206 to risks of patients ofdeveloping pressure injuries.

The identification system 120 may use the trained computing model 130 topredict patients that are developing pressure injuries. For example, ifa patient has more than a threshold risk of developing a pressureinjury, the identification system 120 may generate an alert indicatingthat the patient is at risk of developing a pressure injury.

According to various implementations, the identification system 120 mayidentify root causes of pressure injuries of the patients based on theEMR data 204 and/or the sensor data 206. For example, upon identifyingthat one of the patients has been diagnosed with a pressure injury(e.g., based on the EMR data 204), the identification system 120 mayidentify one or more root causes of the pressure injury based on the EMRdata 204 and/or the sensor data 206. The identification system 120 maygenerate a report indicating the root cause(s) of the pressure injury.In some cases, the identification system 120 may generate the report toindicate root causes of multiple pressure injuries in the clinicalenvironment, which may be conducive for identifying general trends forimproving care within the clinical environment.

The identification system 120 may output the alert(s) and/or report(s)208 to one or more of the first to nth clinical devices 128-1 to 128-n.The alert(s) and/or report(s) 208 can be output by the first to nthclinical devices 128-1 to 128-n. Thus, care providers may be notified ofpatients at risk of developing pressure injuries and/or potentialdeficiencies in care that have led to patients developing pressureinjuries.

Although not specifically illustrated in FIG. 2, the clinical data 202,the EMR data 204, the sensor data 206, the alert(s) and/or report(s)208, or any combination thereof, may be transmitted as data packets overone or more communication networks, such as the communication network(s)118 described above with reference to FIG. 1. Furthermore, the clinicaldata 202, the EMR data 204, the sensor data 206, the alert(s) and/orreport(s) 208, or any combination thereof may be encrypted and/ortransmitted over virtual private networks (VPNs) to maintainconfidentiality of sensitive patient-related medical information.

FIG. 3 illustrates an example of the datastore 124 described above withreference to FIGS. 1 and 2. The datastore 124 stores EMRs of variouspatients in a table 300. In the example illustrated in FIG. 3, EMRs offour different patients are pictured, but implementations are not solimited. In some cases, the datastore 124 stores EMRs of hundreds,thousands, or even millions of patients.

The table 300 includes multiple data fields, such as a patientidentifier 302 field, a note(s) 304 field, a condition 306 field, acondition date 308 field, a nutrition 310 field, and an intake data 312field. In various implementations, the table 300 can store numerousother data fields than the ones illustrated in FIG. 3. As shown in FIG.3, the data fields are arranged in columns and the EMRs of the fourpatients are arranged in rows. The patient identifier 302 field maystore a unique identifier for each of the four patients. The note(s) 304field may store one or more notes associated with conditions of eachpatient. The note(s) 304 may be entered into each EMIR by care providersand may include text that reflects examinations, diagnoses, status,conditions, or any other clinically relevant information, of thepatients. The condition 306 field may store one or more medicalconditions of each patient. The condition date 308 field may indicate adate on which the condition(s) for each patient are diagnosed. Thenutrition 310 field may indicate a nutrition level of each patient. Theintake date 312 field may indicate the date at which a care facilitybegan caring for each patient.

In various cases, the note(s) 304 of each EMR may include text. Forexample, sample Note(s) 4 314 includes text associated with at least oneof an examination, a diagnosis, a condition, a status, or otherclinically relevant information, of Patient 4. To identify whether anyof the note(s) 304 indicate a pressure injury and/or a conditionassociated with a risk of developing a pressure injury, natural languageprocessing may be performed on the note(s) 304. One or more keywordsassociated with pressure injuries and/or risks of developing a pressureinjury may be identified within the note(s) 304. Keywords associatedwith pressure injuries can include, for instance, “wound,” “ulcer,”“blister,” “sore,” “pressure injury,” “ulcer,” “pressure ulcer,”“hospital acquired pressure injury,” “HAPI,” their equivalents, or anycombination thereof. Keywords associated with risks of developingpressure injuries can include, for instance, “reduced mobility,”“prone,” “reduced sensory perception,” “poor circulation,” or anycombination thereof.

For example, Note(s) 4 314 may include a first note and a second note,wherein the second note includes a keyword 316 associated with apressure injury. The keyword 316, for instance, may be the word“blister,” which is indicative of a pressure injury developed by Patient4. Upon identifying the keyword 316 in the Note(s) 314, in some cases,the corresponding condition 306 field of the EMR of Patient 4 can beupdated to reflect “pressure injury.” Further, the condition date 308 ofPatient 4 can be updated to reflect the date of the note among theNote(s) 314 that indicated the keyword 316.

In some cases, other parts of the EMRs can be relied upon to identifywhether patients have developed pressure injuries or are at risk ofdeveloping pressure injuries. For example, the nutrition 310 field of anEMR may be associated with the patient's risk of developing a pressureinjury. A low nutrition level, for example, can correlate to arelatively high risk of developing a pressure injury. In a particularexample, the nutrition 310 of Patient 4 may be Level 4, which may berelatively low (e.g., below a particular threshold). Based on thenutrition 310 field in the EMR of Patient 4, it may be determined thatthe risk of Patient 4 for having and/or developing a pressure injury maybe relatively high. Examples of nutrition 310 levels that can be trackedin the EMRs and which relate to risks of pressure injuries includelevels (e.g., concentrations) of molecules measured in blood, serum, orother bodily fluids (e.g., serum albumin, prealbumin, transferrin,retinal-binding protein, etc.); levels of molecules in food and/orfluids administered to the patients (e.g., calories, protein levels,zinc, Vitamin A, Vitamin C, Vitamin D, arginine, glutamine, etc.);and/or other characteristics of the patients related to nutrition (e.g.,hydration levels of the patient, weight changes of the patient, etc.).In various cases, patients can be at risk of pressure injuries if theirnutrition 310 levels are too high (e.g., higher than a first threshold)and/or too low (e.g., lower than a second threshold).

In some cases, in response to identifying the keyword 316 and/or a lownutrition 310 level, an alert can be generated indicating the pressureinjury of Patient 4 and transmitted to a clinical device associated witha care provider (e.g., a wound care specialist). Thus, automatedreferrals to care providers can be generated by analyzing the EMRsstored in the datastore 124.

In some cases, the condition date 308 field and the intake date 312field can be analyzed to determine whether patients developed or wereplaced at risk of developing pressure injuries in a particular carefacility. If the intake date 312 of a particular patient is before thecondition date 308 of the particular patient, and the condition of theparticular patient includes a pressure injury, then the care facilitymay be responsible for the pressure injury. For example, Patient 1 has apressure injury condition 306, an intake date 312 of Jan. 1, 2020, and acondition date 308 of Jan. 5, 2020. Patient 1 developed the pressureinjury after entering the care facility, and therefore the care facilitymay be responsible for the pressure injury of Patient 1. On the otherhand, Patient 3 has a pressure injury condition 306, an intake date 312of Jan. 1, 2020, and a condition date 308 of Dec. 15, 2019. Because theintake date 312 of Patient 3 is after the condition date 308 of Patient3, the care facility may not be responsible for the pressure injury ofPatient 3. In some cases, a report indicating pressure injuries that thecare facility is responsible for can be generated. Accordingly, in theexample of FIG. 3, the report may omit features of the EMR of Patient 3.Further, the report may omit features of the EMRs of patients withoutpressure injuries, such as Patient 2, whose condition 306 is a “surgicalwound” rather than a pressure injury.

In various examples, alert(s) and/or report(s) can be generated by anidentification system (e.g., the identification system 120), an EMRsystem (e.g., the EMR system 122), or a combination thereof. Forexample, the identification system and/or the EMR system may analyze theEMRs stored in the datastore 124 to generate alert(s) indicating thatone or more patients have pressure injuries or are at risk of developingpressure injuries, to generate report(s) indicating trends associatedwith pressure injuries developed in a care facility.

FIG. 4 illustrates an example process 400 for training a computing modelto predict a pressure injury based on sensor data and/or EMR data. Theprocess 400 can be performed by an entity including the support device104, the identification system 120, the EMR system 122, the clinicaldevice 128, or any combination thereof. For example, the process 400 canbe performed by one or more computing devices including memory thatstores instructions and one or more processors that, when executing theinstructions, perform the operations of the process 400.

At 402, the entity can identify sensor data and/or EMR data of firstpatients. Sensor data may include data indicating one or more parametersof the first patients. The parameter(s) include, for instance, movementsof the first patients, moisture of support structures (e.g., beds) ofthe first patients, nutrition levels of the first patients, temperaturesof the first patients, or any other metric relevant to pressure injurysusceptibility. In some cases, the sensor data is generated by one ormore sensors including load cells configured to detect a pressure of thefirst patients on support structures (e.g., beds) of the first patients,moisture sensors configured to detect moisture on the supportstructures, temperature sensors configured to detect temperatures of thefirst patients, cameras (e.g., infrared cameras, visible light cameras,radar sensors, or some other type of camera) configured to generateinfrared images and/or videos of the patient, or any combinationthereof.

EMR data may include data stored in EMRs of the first patients. The EMRdata may include nutrition levels of the first patients, medicalhistories of the first patients, notes written by care providers aboutthe first patients, and the like. In some cases, natural languageprocessing can be performed on the notes. For example, one or morekeywords associated with pressure injuries or risks of pressure injuriescan be identified in the notes.

At 404, the entity can identify whether the first patients developedpressure injuries. In some implementations, the EMR data may indicateone or more of the first patients that developed pressure injuries. Forexample, the pressure injuries may be indicated as diagnosed conditionswithin the EMRs of the first patients. In some examples, the pressureinjuries may be documented in notes written by care providers about thefirst patients. The entity, in some cases, may identify one or morekeywords in the notes that are indicative of the first patients havingpressure injuries.

At 406, the entity can train a computing model to predict a pressureinjury of a second patient. The computing model may be a machinelearning model, such as a linear regression model, a logistic regressionmodel, a gradient boost machine, a deep learning model, or a combinationthereof. In various examples the entity can optimize the computing modelbased on the sensor data, the EMR data, and indications of whether thefirst patients developed pressure injuries. For example, one or morefactors within the computing model can be optimized such that thecomputing model accurately predicts whether the first patients developedthe pressure injuries based on inputs including the sensor data and theEMR data of the first patients.

FIG. 5 illustrates an example process 500 for predicting a pressureinjury of a patient. The process 500 can be performed by an entityincluding the support device 104, the identification system 120, the EMRsystem 122, the clinical device 128, or any combination thereof. Forexample, the process 500 can be performed by one or more computingdevices including memory that stores instructions and one or moreprocessors that, when executing the instructions, perform the operationsof the process 500.

At 502, the entity can identify sensor data of the patient. Sensor datamay include data indicating one or more parameters of the patient. Theparameter(s) include, for instance, movements of the patient, moistureof support structures (e.g., beds) of the patient, nutrition levels ofthe patient, temperatures of the patient, or any other metric relevantto pressure injury susceptibility. In some cases, the sensor data isgenerated by one or more sensors including load cells configured todetect a pressure of the patient on the support structure, moisturesensors configured to detect moisture on the support structure,temperature sensors configured to detect temperatures of the patient,cameras (e.g., infrared cameras, visible light cameras, radar sensors,or other types of cameras) configured to generate infrared images and/orvideos of the patient, or any combination thereof. The sensors maydetect, for instance, parameter(s) at different times and/or differentpositions with respect to the body of the patient.

At 504, the entity can identify EMR data of the patient. EMR data mayinclude data stored in an EMR of the patient. The EMR data may includenutrition levels of the patient, a medical history of the patient, noteswritten by care providers about the patient, and the like. In somecases, natural language processing can be performed on the notes. Forexample, one or more keywords associated with pressure injuries or risksof pressure injuries can be identified in the notes.

At 506, the entity can calculate a score indicative of a risk that thepatient has or will develop a pressure injury based on the sensor dataand the EMR data. In some cases, the score is calculated by inputtingparameters indicated by the sensor data and the EMR data into acomputing model, such as an equation or a trained machine learningmodel. In some cases, the output of the equation or the machine learningmodel may be the score. In some cases, the score is represented as apercentage corresponding to the risk that the patient has or willdevelop a pressure injury. In some cases, the score corresponds to aBraden score of the patient. In various implementations, the scoreindicates that the patient is in danger of developing a pressure injury.For example, the entity determines that the patient has greater than athreshold likelihood (e.g., 50%) of developing a pressure injury withina time period (e.g., a day, a week, etc.).

At 508, the entity can output an alert based on the score. The alert mayindicate the risk that the patient has or will develop the pressureinjury. In some examples, the alert includes the score. In variousexamples, the alert indicates that the patient is in danger ofdeveloping the pressure injury. The alert, for example, provides aninstruction for safe patient handling, providing an improved surface(e.g., placing a cushion, tape, or the like between an object and thepatient), consulting an expert (e.g., a WOCN), turning the patient,adding or replacing pads to the support structure of the patient,mobilizing the patient, adjusting nutrition of the patient, modifying anincontinence pad changing schedule, or a combination thereof. Accordingto some implementations, the alert can be output (e.g., pushed) to acomputing device associated with a care provider. The alert may instructthe care provider to attend to the patient. Accordingly, the pressureinjury may be prevented. In some examples, the score and/or risk may bestored in the EMR of the patient.

FIG. 6 illustrates an example process 600 for predicting a pressureinjury of a patient. The process 500 can be performed by an entityincluding the support device 104, the identification system 120, the EMRsystem 122, the clinical device 128, or any combination thereof. Forexample, the process 600 can be performed by one or more computingdevices including memory that stores instructions and one or moreprocessors that, when executing the instructions, perform the operationsof the process 600.

At 602, the entity can identify that a patient has developed a pressureinjury. In various cases, the entity can identify the pressure injurybased on an EMR of the patient. The EMR may be stored in a datastore(e.g., a database). In some cases, the EMR of the patient is stored withEMRs of other patients in the datastore. According to someimplementations, the entity can perform natural language processing onat least one note in the EMR of the patient. The entity may identifythat the patient has developed the pressure injury if the note(s)include one or more keywords associated with the pressure injury.

At 604, the entity can identify sensor data and/or EMR data of thepatient. Sensor data may include data indicating one or more parametersof the patient. The parameter(s) include, for instance, movements of thepatient, moisture of support structures (e.g., beds) of the patient,nutrition levels of the patient, temperatures of the patient, or anyother type of metric relevant to pressure injury susceptibility. In somecases, the sensor data is generated by one or more sensors includingload cells configured to detect a pressure of the patient on the supportstructure, moisture sensors configured to detect moisture on the supportstructure, temperature sensors configured to detect temperatures of thepatient, cameras (e.g., infrared cameras, visible light cameras, radarsensors, depth cameras, or other types of light sensors) configured togenerate images and/or videos of the patient, or any combinationthereof. The sensors may detect, for instance, parameter(s) at differenttimes and/or different positions with respect to the body of thepatient.

EMR data may include data stored in an EMR of the patient. The EMR datamay include nutrition levels of the patient, a medical history of thepatient, notes written by care providers about the patient, and thelike. In some cases, natural language processing can be performed on thenotes. For example, one or more keywords associated with pressureinjuries or risks of pressure injuries can be identified in the notes.

At 606, the entity can identify one or more root causes of the pressureinjury based on the sensor data and/or the EMR data. In some cases, theroot cause(s) include one or more risk factors that the patientexperienced prior to the development of the pressure injury. In somecases, these risk factors can be identified based on the sensor dataand/or the EMR data. For instance, a parameter indicated by the sensordata and/or the EMR data may be compared to a threshold. Based on thecomparison, the entity may conclude that a risk factor associated withthe parameter is a root cause of the pressure injury. According to someexamples, the entity may conclude that the risk factor is a root causeif the parameter is within a threshold range (e.g., above and/or below athreshold) for greater than a particular time period (e.g., one hour,one day, or some other time period). In some cases, the entity canidentify the root cause(s) by inputting the sensor data and/or the EMRdata into a trained machine learning model, wherein an output of thetrained machine learning model indicates the root cause(s).

At 608, the entity can output a report indicating the root cause(s).According to some examples, the report can indicate the root cause(s) ofthe pressure injury of the patient as well as root causes of otherpressure injuries experienced by other patients in a care facility.Accordingly, the report can indicate trends associated with pressureinjuries in the care facility. In some examples, the report can beoutput to a computing device, such as a computing device associated withan administrator and/or care provider. Using the information containedin the report, the administrator and/or care provider can take effortsto prevent future pressure injuries in the care facility and/or treatthe pressure injury of the patient. In some examples, the entity canfurther update the EMR to indicate the root cause(s) of the patient.

FIG. 7 illustrates at least one example device 700 configured to enableand/or perform the some or all of the functionality discussed herein.Further, the device(s) 700 can be implemented as one or more servercomputers 702, a network element on a dedicated hardware, as a softwareinstance running on a dedicated hardware, or as a virtualized functioninstantiated on an appropriate platform, such as a cloud infrastructure,and the like. It is to be understood in the context of this disclosurethat the device(s) 700 can be implemented as a single device or as aplurality of devices with components and data distributed among them.

As illustrated, the device(s) 700 comprise a memory 704. In variousembodiments, the memory 704 is volatile (including a component such asRandom Access Memory (RAM)), non-volatile (including a component such asRead Only Memory (ROM), flash memory, etc.) or some combination of thetwo.

The memory 704 may include various components, such the identificationsystem 120, the EMR system 122, the datastore 124, the computing model130, and the like. Any of the identification system 120, the EMR system122, the datastore 124, the computing model 130 can include methods,threads, processes, applications, or any other sort of executableinstructions. The identification system 120, the EMR system 122, thedatastore 124, the computing model 130 and various other elements storedin the memory 704 can also include files and databases.

The memory 704 may include various instructions (e.g., instructions theidentification system 120, the EMR system 122, the datastore 124, and/orthe computing model 130), which can be executed by at least oneprocessor 706 to perform operations. In some embodiments, theprocessor(s) 706 includes a Central Processing Unit (CPU), a GraphicsProcessing Unit (GPU), or both CPU and GPU, or other processing unit orcomponent known in the art.

The device(s) 700 can also include additional data storage devices(removable and/or non-removable) such as, for example, magnetic disks,optical disks, or tape. Such additional storage is illustrated in FIG. 7by removable storage 708 and non-removable storage 710. Tangiblecomputer-readable media can include volatile and nonvolatile, removableand non-removable media implemented in any method or technology forstorage of information, such as computer readable instructions, datastructures, program modules, or other data. The memory 704, removablestorage 708, and non-removable storage 710 are all examples ofcomputer-readable storage media. Computer-readable storage mediainclude, but are not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, Digital Versatile Discs (DVDs),Content-Addressable Memory (CAM), or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the device(s) 700. Anysuch tangible computer-readable media can be part of the device(s) 700.

The device(s) 700 also can include input device(s) 712, such as akeypad, a cursor control, a touch-sensitive display, voice input device,etc., and output device(s) 714 such as a display, speakers, printers,etc. These devices are well known in the art and need not be discussedat length here. In particular implementations, a user can provide inputto the device(s) 700 via a user interface associated with the inputdevice(s) 712 and/or the output device(s) 714. In some cases, the inputdevice(s) 712 include one or more sensors, such as the load cell(s) 106,the moisture sensor(s) 108, the temperature sensor(s) 110, the camera(s)112, the video camera(s) 114, or any combination thereof.

As illustrated in FIG. 7, the device(s) 700 can also include one or morewired or wireless transceiver(s) 716. For example, the transceiver(s)716 can include a Network Interface Card (NIC), a network adapter, a LANadapter, or a physical, virtual, or logical address to connect to thevarious base stations or networks contemplated herein, for example, orthe various user devices and servers. To increase throughput whenexchanging wireless data, the transceiver(s) 716 can utilizeMultiple-Input/Multiple-Output (MIMO) technology. The transceiver(s) 716can include any sort of wireless transceivers capable of engaging inwireless, Radio Frequency (RF) communication. The transceiver(s) 716 canalso include other wireless modems, such as a modem for engaging inWi-Fi, WiMAX, Bluetooth, or infrared communication.

In some implementations, the transceiver(s) 716 can be used tocommunicate between various functions, components, and modules that arecomprised in the device(s) 700. For instance, the transceivers 716 mayfacilitate communications between the identification system 120, the EMRsystem 122, the datastore 124, the computing model 130, and the like. Insome cases, the transceiver(s) 716 include the transmitter 116.

In some instances, one or more components may be referred to herein as“configured to,” “configurable to,” “operable/operative to,”“adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Thoseskilled in the art will recognize that such terms (e.g., “configuredto”) can generally encompass active-state components and/orinactive-state components and/or standby-state components, unlesscontext requires otherwise.

As used herein, the term “based on” can be used synonymously with“based, at least in part, on” and “based at least partly on.”

As used herein, the terms “comprises/comprising/comprised” and“includes/including/included,” and their equivalents, can be usedinterchangeably. An apparatus, system, or method that “comprises A, B,and C” includes A, B, and C, but also can include other components(e.g., D) as well. That is, the apparatus, system, or method is notlimited to components A, B, and C.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described.

EXAMPLE CLAUSES

-   -   1. A pressure injury identification system, including: a bed        configured to support a patient; one or more sensors coupled to        the bed and configured to detect one or more parameters of the        patient, and to generate sensor data based on the one or more        parameters; a transceiver; at least one processor operably        connected to the transceiver and the one or more sensors; and        memory operably connected to the at least one processor, the        memory storing: a database including an electronic medical        record (EMR) of the patient; and instructions that, when        executed by the at least one processor, cause the at least one        processor to perform operations including: determining, based on        the EMR of the patient, that the patient has a pressure injury;        determining, based on the sensor data, at least one root cause        of the pressure injury; updating the EMR to indicate the at        least one root cause of the pressure injury; and causing the        transceiver to output, to an external computing device, a report        indicating the pressure injury and the at least one root cause        of the pressure injury.    -   2. The pressure injury identification system of clause 1,        wherein the one or more sensors includes at least one of a load        cell configured to detect a pressure of the patient on the bed,        a moisture sensor configured to detect moisture on the bed, a        temperature sensor configured to detect a temperature of the        patient, an infrared camera configured to generate at least one        infrared image of the patient, or a video camera configured to        generate at least one video of the patient.    -   3. The pressure injury identification system of clause 1 or 2,        wherein determining the at least one root cause of the pressure        injury includes: determining a time at which the EMR was updated        to indicate the pressure injury; determining, based on the        sensor data, that the patient experienced a risk factor        associated with the pressure injury prior to the time at which        the EMR was updated to indicate the pressure injury; and        determining the at least one root cause based on the risk        factor.    -   4. A method, including: determining, based on an electronic        medical record (EMR) of a patient, that the patient has an        injury; receiving, from one or more sensors, sensor data        indicating one or more parameters of the patient; determining,        based on the sensor data, at least one root cause of the injury;        and transmitting, to an external computing device, a report        indicating the injury and the at least one root cause of the        injury.    -   5. The method of clause 4, wherein determining that the patient        has the injury includes: identifying a note in the EMR of the        patient; and identifying, in the note, at least one keyword        associated with the injury.    -   6. The method of clause 4 or 5, wherein the one or more sensors        are in a bed supporting the patient, the one or more sensors        including at least one of a load cell, a moisture sensor, a        temperature sensor, an infrared camera, or a video camera.    -   7. The method of any one of clauses 4 to 6, wherein the one or        more parameters of the patient include at least one of a        movement of the patient, a moisture of a bed supporting the        patient, a nutrition level of the patient, or a temperature of        the patient.    -   8. The method of any one of clauses 4 to 7, wherein determining        the at least one root cause of the injury includes: determining        that the one or more parameters are within one or more threshold        ranges for greater than one or more time periods.    -   9. The method of any one of clauses 4 to 8, wherein determining        the at least one root cause of the injury includes: inputting        the sensor data into a trained machine learning model; and        determining the at least one root cause of the injury based on        an output of the trained machine learning model.    -   10. The method of any one of clauses 4 to 9, further including:        determining, based on the EMR of the patient, a nutrition level        of the patient, wherein determining the at least one root cause        of the injury includes: determining that the nutrition level of        the patient has remained under a threshold level for greater        than a time period; and determining that the nutrition level of        the patient is an additional root cause of the injury.    -   11. The method of any one of clauses 4 to 10, wherein        determining the at least one root cause of the pressure injury        includes: determining a time at which the EMR was updated to        indicate the pressure injury; determining, based on the sensor        data, that the patient experienced a risk factor associated with        the pressure injury prior to the time at which the EMR was        updated to indicate the pressure injury; and determining the at        least one root cause based on the risk factor.    -   12. The method of any one of clauses 4 to 11, the patient being        a first patient, the injury being a first injury, the EMR being        a first EMR, the method further including: identifying, based on        second EMRs of second patients, that the second patients have        second injuries; determining that the second injuries have the        same type of root cause; and transmitting, to an external        computing device, a report indicating the type of root cause of        the second injuries.    -   13. A system, including: at least one processor; and memory        operably connected to the at least one processor, the memory        storing instructions that, when executed by the at least one        processor, cause the at least one processor to perform        operations including: receiving, from one or more sensors,        sensor data indicating one or more parameters of the patient,        the one or more sensors including a camera, the sensor data        including images of the patient over a time period; predicting,        based on the sensor data, that the patient is in danger of        acquiring a pressure injury; updating an electronic medical        record (EMR) of the patient to indicate the pressure injury; and        transmitting, to an external computing device, an alert        indicating that the patient is in danger of acquiring the        pressure injury.    -   14. The system of clause 13, wherein the one or more sensors are        included in a bed supporting the patient, the one or more        sensors at least one of a load cell, a pressure sensor, a        sensing pad, a moisture sensor, or a temperature sensor, and        wherein the camera includes an infrared camera, a depth-sensing        camera, or a video camera.    -   15. The system of clause 13 or 14, wherein the one or more        parameters of the patient include a movement of the patient        indicated by the images.    -   16. The system of any one of clauses 13 to 15, wherein the one        or more parameters of the patient include at least one of a        moisture of a bed supporting the patient, a nutrition level of        the patient, or a temperature of the patient.    -   17. The system of any one of clauses 13 to 16, wherein        determining that the patient is in danger of acquiring the        pressure injury includes: calculating a pressure injury score        based on the one or more parameters, the pressure injury score        corresponding to a likelihood that the patient will acquire the        pressure injury; and determining that the pressure injury score        exceeds a threshold.    -   18. The system of any one of clauses 13 to 17, wherein        determining that the patient is in danger of acquiring the        pressure injury includes: inputting the sensor data into a        trained machine learning model; and determining that the patient        is in danger of acquiring the pressure injury based on an output        of the trained machine learning model.    -   19. The system of any one of clauses 13 to 18, wherein the        operations further include: determining, based on the EMR of the        patient, a nutrition level of the patient, and wherein        determining that the patient is in danger of acquiring the        pressure injury further includes: determining that the nutrition        level of the patient was below a threshold for greater than a        time period.    -   20. The system of any one of clauses 13 to 19, wherein the        operations further include: determining that the patient has        acquired the pressure injury; and determining, based on the        sensor data, at least one root cause of the pressure injury, and        wherein the report further indicates the at least one root cause        of the pressure injury.    -   21. The system of any one of clauses 13 to 20, wherein        predicting, based on the sensor data, that the patient is in        danger of acquiring a pressure injury includes predicting that a        body part of the patient is in danger of acquiring the pressure        injury, and wherein the alert indicates that the body part of        the patient is in danger of acquiring the pressure injury.    -   22. The system of any one of clauses 13 to 21, wherein the alert        further indicates one or more recommended interventions        associated with reducing a risk that the patient will acquire        the pressure injury.    -   23. A system, including: at least one processor; and memory        operably connected to the at least one processor, the memory        storing instructions that, when executed by the at least one        processor, cause the at least one processor to perform        operations including: receiving, from sensors, sensor data        indicating one or more parameters of multiple patients in a        unit, the sensors including one or more cameras, the sensor data        including images of the patients over a time period; predicting,        based on the sensor data, that a number of the patients are in        danger of acquiring pressure injuries; generating an alert based        on the number of the patients that are in danger of acquiring        the pressure injuries, the alert including a recommended        intervention that includes at least one of an instruction to        assign staff to the unit or to provide equipment to the unit;        and transmitting, to an external computing device, the alert.

1. A pressure injury identification system, comprising: a bed configuredto support a patient; one or more sensors coupled to the bed andconfigured to detect one or more parameters of the patient, and togenerate sensor data based on the one or more parameters; a transceiver;at least one processor operably connected to the transceiver and the oneor more sensors; and memory operably connected to the at least oneprocessor, the memory storing: a database comprising an electronicmedical record (EMR) of the patient; and instructions that, whenexecuted by the at least one processor, cause the at least one processorto perform operations comprising: determining, based on the EMR of thepatient, that the patient has a pressure injury; determining, based onthe sensor data, at least one root cause of the pressure injury;updating the EMR to indicate the at least one root cause of the pressureinjury; and causing the transceiver to output, to an external computingdevice, a report indicating the pressure injury and the at least oneroot cause of the pressure injury.
 2. The pressure injury identificationsystem of claim 1, wherein the one or more sensors comprises at leastone of a load cell configured to detect a pressure of the patient on thebed, a moisture sensor configured to detect moisture on the bed, atemperature sensor configured to detect a temperature of the patient, aninfrared camera configured to generate at least one infrared image ofthe patient, or a video camera configured to generate at least one videoof the patient.
 3. The pressure injury identification system of claim 1,wherein determining the at least one root cause of the pressure injurycomprises: determining a time at which the EMR was updated to indicatethe pressure injury; determining, based on the sensor data, that thepatient experienced a risk factor associated with the pressure injuryprior to the time at which the EMR was updated to indicate the pressureinjury; and determining the at least one root cause based on the riskfactor.
 4. A method, comprising: determining, based on an electronicmedical record (EMR) of a patient, that the patient has an injury;receiving, from one or more sensors, sensor data indicating one or moreparameters of the patient; determining, based on the sensor data, atleast one root cause of the injury; and transmitting, to an externalcomputing device, a report indicating the injury and the at least oneroot cause of the injury.
 5. The method of claim 4, wherein determiningthat the patient has the injury comprises: identifying a note in the EMRof the patient; and identifying, in the note, at least one keywordassociated with the injury.
 6. The method of claim 4, wherein the one ormore sensors are in a bed supporting the patient, the one or moresensors comprising at least one of a load cell, a moisture sensor, atemperature sensor, an infrared camera, or a video camera, and whereinthe one or more parameters of the patient comprise at least one of amovement of the patient, a moisture of a bed supporting the patient, anutrition level of the patient, or a temperature of the patient.
 7. Themethod of claim 4, wherein determining the at least one root cause ofthe injury comprises: determining that the one or more parameters arewithin one or more threshold ranges for greater than one or more timeperiods.
 8. The method of claim 4, wherein determining the at least oneroot cause of the injury comprises: inputting the sensor data into atrained machine learning model; and determining the at least one rootcause of the injury based on an output of the trained machine learningmodel.
 9. The method of claim 4, further comprising: determining, basedon the EMR of the patient, a nutrition level of the patient, whereindetermining the at least one root cause of the injury comprises:determining that the nutrition level of the patient has remained under athreshold level for greater than a time period; and determining that thenutrition level of the patient is an additional root cause of theinjury.
 10. The method of claim 4, wherein determining the at least oneroot cause of the pressure injury comprises: determining a time at whichthe EMR was updated to indicate the pressure injury; determining, basedon the sensor data, that the patient experienced a risk factorassociated with the pressure injury prior to the time at which the EMRwas updated to indicate the pressure injury; and determining the atleast one root cause based on the risk factor.
 11. The method of claim4, the patient being a first patient, the injury being a first injury,the EMIR being a first EMR, the method further comprising: identifying,based on second EMRs of second patients, that the second patients havesecond injuries; determining that the second injuries have the same typeof root cause; and transmitting, to an external computing device, areport indicating the type of root cause of the second injuries.
 12. Asystem, comprising: at least one processor; and memory operablyconnected to the at least one processor, the memory storing instructionsthat, when executed by the at least one processor, cause the at leastone processor to perform operations comprising: receiving, from one ormore sensors, sensor data indicating one or more parameters of thepatient, the one or more sensors comprising a camera, the sensor datacomprising images of the patient over a time period; predicting, basedon the sensor data, that the patient is in danger of acquiring apressure injury; updating an electronic medical record (EMR) of thepatient to indicate the pressure injury; and transmitting, to anexternal computing device, an alert indicating that the patient is indanger of acquiring the pressure injury.
 13. The system of claim 12,wherein the one or more sensors are comprised in a bed supporting thepatient, the one or more sensors at least one of a load cell, a pressuresensor, a sensing pad, a moisture sensor, or a temperature sensor, andwherein the camera comprises an infrared camera, a depth-sensing camera,or a video camera.
 14. The system of claim 12, wherein the one or moreparameters of the patient comprise at least one of a movement of thepatient indicated by the images, a moisture of a bed supporting thepatient, a nutrition level of the patient, or a temperature of thepatient.
 15. The system of claim 12, wherein determining that thepatient is in danger of acquiring the pressure injury comprises:calculating a pressure injury score based on the one or more parameters,the pressure injury score corresponding to a likelihood that the patientwill acquire the pressure injury; and determining that the pressureinjury score exceeds a threshold.
 16. The system of claim 12, whereindetermining that the patient is in danger of acquiring the pressureinjury comprises: inputting the sensor data into a trained machinelearning model; and determining that the patient is in danger ofacquiring the pressure injury based on an output of the trained machinelearning model.
 17. The system of claim 12, wherein the operationsfurther comprise: determining, based on the EMR of the patient, anutrition level of the patient, and wherein determining that the patientis in danger of acquiring the pressure injury further comprises:determining that the nutrition level of the patient was below athreshold for greater than a time period.
 18. The system of claim 12,wherein the operations further comprise: determining that the patienthas acquired the pressure injury; and determining, based on the sensordata, at least one root cause of the pressure injury, and wherein thereport further indicates the at least one root cause of the pressureinjury.
 19. The system of claim 12, wherein predicting, based on thesensor data, that the patient is in danger of acquiring a pressureinjury comprises predicting that a body part of the patient is in dangerof acquiring the pressure injury, and wherein the alert indicates thatthe body part of the patient is in danger of acquiring the pressureinjury.
 20. The system of claim 12, wherein the alert further indicatesone or more recommended interventions associated with reducing a riskthat the patient will acquire the pressure injury.